Conventional maximum power point tracking (MPPT) methods are ineffective under partially shaded conditions because multiple local maximum can be exhibited on power-voltage characteristic curve. This study proposes an improved cuckoo search (ICS) MPPT method after investigating the cuckoo search (CS) algorithm applied in solving multiple MPPT. The algorithm eliminates the random step in the original CS algorithm, and the conception of low-power, high-power, normal and marked zones are introduced. The adaptive step adjustment is also realized according to the different stages of the nest position. This algorithm adopts the large step in low-power and marked zones to reduce search time, and a small step in high-power zone is used to improve search accuracy. Finally, simulation and experiment results indicate that the promoted ICS algorithm can immediately and accurately track the global maximum under partially shaded conditions, and the array output efficiency can be improved.

Under partial shading conditions (PSCs), multiple local maximum power points (MPPs) may be exhibited on the P-U curve of photovoltaic systems. Direct control (DIRC) methods cannot extract the global MPP (GMPP); soft computing techniques can achieve it but are time consuming. This paper proposes a novel hybrid maximum power point tracking (MPPT) algorithm (INC-FA) combining incremental conductance (INC) and firefly algorithm (FA) to achieve better adaptability in various environments. INC is widely used because of its low-cost implementation and stability under rapidly changing atmospheric conditions, while FA is efficient in searching the GMPP. This combination (INC-FA) not only enables a faster global searching capability but also performs well as a DIRC method in the case of a single peak. In addition, INC-FA introduces the concept of the global optimal region and devises the population initialization mechanism to determine the initial position and population size of fireflies. Finally, the proposed algorithm is compared with three other MPPT methods under four different conditions. Simulation and experiment results demonstrate that the proposed algorithm can track the GMPP under various conditions with higher speed and accuracy.

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Under partial shading conditions, the power–voltage (P-U) curve may exhibit multiple local maxima. This makes it challenging to track the global maximum power point (GMPP). Additionally, in such conditions, conventional maximum power point tracking (MPPT) methods cannot be used to extract the GMPP. This paper describes a modified firefly algorithm (MFA) that can rapidly and accurately extract the GMPP under partial shading conditions. The algorithm introduces the concepts of the global and local firefly densities during each iteration, and devises two elimination mechanisms to adaptively adjust the firefly population. The proposed method is compared with the traditional MPPT algorithms under four different partial shading conditions. Simulation results demonstrate that the MFA can immediately and accurately track the global maximum under the partially shaded conditions, and that the proposed method outperforms conventional techniques in terms of tracking efficiency and speed.

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